A Novel Honey Badger Algorithm with Multilayer Perceptron for Forecasting COVID-19 Time Series Data

The COVID-19 pandemic was affected the health, economy, and all aspects of human lives around the world. Accurate prediction of the new daily cases of COVID-19 is critical for precise programming and needed measures to prevent the outbreak of it. Hence, in the present paper, we implement a new hybrid intelligent model, namely the artificial neural network (ANN) hybridized with the Honey Badger Algorithm (HBA-ANN) for accurately daily new cases COVID-19 prediction in Brazil, India, Russia, and the USA. The performance of the hybrid model was compared with the stand-alone ANN and Gene Expression Programming (GEP) model using statistical (R 2 , RMSE, SI, and NSE) and graphical (Taylor and scatter diagrams and box plot) criteria. Results showed that the HBA-ANN model with the high value of R 2 , law value of RMSE, and the least distance from actual values outperformed the ANN and GEP models in each country. Hence, it is recommended to implement the HBA algorithm to increase the prediction accuracy of the models in medicine field.